Understanding and Applying Text Embeddings

What You'll Learn

• Use text embeddings to capture the meaning of sentences and paragraphs.
• Apply text embeddings for tasks like text clustering, classification, and outlier detection.
• Use Google Cloud’s Vertex AI to build a question-answering system.

About This Course

This course covers the Vertex AI Text-Embeddings API and its application for generating and using text embeddings, which numerically represent text meaning. By the end, you’ll understand how to leverage embeddings for clustering, classification, and Q&A systems, enhancing various real-world applications.

• Understand word and sentence embeddings and their properties.
• Measure semantic similarity between text using embeddings.
• Use embeddings for text classification, clustering, and outlier detection.
• Adjust LLM parameters (temperature, top-k, top-p) for text generation.
• Apply ScaNN for efficient semantic search.
• Build a Q&A system by combining semantic search with an LLM.

By completing this course, you’ll be proficient in using text embeddings and integrating them into common LLM applications for improved NLP outcomes.

Course Outline

• Introduction
  Overview of text embeddings and their applications in NLP.
• Getting Started With Text Embeddings
  Introduction to text embeddings with practical code examples to generate embeddings.
• Understanding Text Embeddings
  Insight into the mathematical properties and structure of embeddings.
• Visualizing Embeddings
  Techniques for visualizing text embeddings using code examples.
• Applications of Embeddings
  Practical applications such as classification, clustering, and outlier detection.
• Text Generation with Vertex AI
  Using Vertex AI for text generation, exploring parameter adjustments for LLMs.
• Building a Q&A System Using Semantic Search
  Integrating semantic search with an LLM for building a question-answering system.
• Optional - Google Cloud Setup
  Setting up Google Cloud services and environments for course activities.
• Conclusion
  Recap of course concepts and applications of text embeddings.

Who Should Join?

This course is ideal for anyone with basic Python knowledge looking to apply text embeddings to NLP tasks such as clustering, classification, and semantic search.